Nima Nikvand

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A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a general-purpose denoising in the sense that it does not need to employ any particular assumption on the structure of the(More)
Image similarity measurement is a fundamental and common issue in a broad range of problems in image processing, compression, communication, recognition and retrieval. Existing image similarity measures are limited to restricted application environments. The theory of Kolmogorov complexity and the related normalized information distance (NID) measure(More)
Image distortion analysis is a fundamental issue in many image processing problems, including compression, restoration, recognition, classification, and retrieval. Traditional image distortion evaluation approaches tend to be heuristic and are often limited to specific application environment. In this work, we investigate the problem of image distortion(More)
There has been a growing recent interest of applying Kol-mogorov complexity and its related normalized information distance (NID) measures in real-world problems, but their application in the field of medical image processing remains limited. In this work we attempt to incorporate NID in the design of windowing operators for optimal visualization of high(More)
Data Denoising by Noise Invalidation c © Nima Nikvand, 2008 Master of Applied Science (MASc) Department of Electrical and Computer Engineering Ryerson University In this thesis, the problem of data denoising is studied, and two new denoising approaches are proposed. Using statistical properties of the additive noise, the methods provide adaptive(More)
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